FIGS. 1a to 1d are each an image of a person wearing striped clothing and are typical of catalogue images of models wearing the clothes. FIGS. 1a and 1b show males wearing striped T-shirts; FIG. 1a with blue and white stripes and FIG. 1b with black and white stripes. FIG. 1c shows a female wearing a black and white striped dress and FIG. 1d shows a female wearing a blue and white striped skirt. The scale of the images in FIGS. 1a to 1c are approximately the same whereas FIG. 1d has a smaller scale because it is “zoomed out” to show the entire model.
FIGS. 1a to 1d are examples of images which may be used to train a learning system such as a convolutional neural network, auto-encoder or other neural network or the like to classify the pattern contained in images. In the case of the convolutional neural network, a very large labeled training set is required because otherwise there is not enough data available to the neural network to help it learn which aspects of the images are pertinent. In this case, the striped pattern is the key element and other aspects such as the hair-color of the models or the shoes they are wearing are irrelevant. In the case of an unsupervised network such as an auto-encoder, the network would learn to cluster the images according to a whole range of factors, of which pattern may well be one, but it would not be an efficient (or effective) way to learn to classify (or search) images based on pattern alone.
The present invention seeks to provide a more efficient method for training a learning system to identify patterns (or other characteristics) within an image and once trained, to search for related images.